[1]} yolo is a popular one-stage object detector proposed by Redmon et al. in 2015, casting object detection as a regression problem from image pixels to spatially separated bounding boxes and associated class probabilities.
Since the region proposal generation stage is completely drained, yolo directly predicts detections using a small set of candidate regions. In particular, yolo divides an input image into a \(S \times S\)  regular grid. Each grid cell is responsible for predicting B objects encoded in 5 terms: x, y, w, h, and a confidence score. The (x, y) coordinates represent the center of the box enclosing the object relative to the bounds of the grid cell. The width and height are instead predicted relatively to the whole image. Finally, the confidence score tells us how certain the predicted bounding box encloses some object. This score does not say anything about what kind of object is in the box, just if the shape of the box is any good. Each grid cell also predicts C conditional class probabilities. These probabilities are conditioned on the grid cell containing an object. yolo only predicts one set of class probabilities per grid cell, regardless of the number of boxes B, and builds a so-called class probability map. At test time, the confidence score for the bounding box and the class prediction are combined into one final score that tells us the probability that this bounding box contains a specific type of object. Since, in the end, yolo predicts \(S \times S \times B\)  bounding boxes, additional filtering using nms is needed.
